{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "leading-bleeding",
   "metadata": {},
   "source": [
    "# 测试验证彩票预测的模型-2"
   ]
  },
  {
   "cell_type": "raw",
   "id": "narrative-trinidad",
   "metadata": {},
   "source": [
    "第2种模型：\n",
    "当做分类问题预测\n",
    "红球与篮球分开,以红球为例\n",
    "将红球从1～35分成35类\n",
    "每天一类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "tamil-prevention",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 依赖导入\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "official-nightlife",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['Songti SC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "alive-street",
   "metadata": {},
   "outputs": [
    {
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       "    0    1    2    3    4    5    6    7    8    9   ...   25   26   27   28  \\\n",
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       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../red_ball_one_hot.csv\", header=None, index_col=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "brilliant-projection",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据拆分两部分（训练集和测试集）\n",
    "features = 35\n",
    "time_step = 10\n",
    "slide = int((len(data)*0.8))\n",
    "train_data = np.array(data.iloc[:slide])\n",
    "test_data = np.array(data.iloc[slide:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "passive-holly",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据抽取方法\n",
    "def data_extract(data, time_step=10):\n",
    "    x = []\n",
    "    y = []\n",
    "    for i in range(len(data) - time_step):\n",
    "        x.append([a for a in data[i: i + time_step]])\n",
    "        y.append(data[i+time_step])\n",
    "    x = np.array(x).reshape(len(x), len(x[0]), features)\n",
    "    y = np.array(y).reshape(len(y), features)\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "strong-ghost",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      "   1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1.]\n",
      "  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      "   0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]\n",
      "  [0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.\n",
      "   0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.\n",
      "   0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "  [1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      "  [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.\n",
      "   0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
      "   0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "  [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1.\n",
      "   0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "  [1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "  [0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]]\n",
      "\n",
      " [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      "   0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]\n",
      "  [0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.\n",
      "   0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.\n",
      "   0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "  [1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      "  [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.\n",
      "   0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
      "   0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "  [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1.\n",
      "   0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "  [1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "  [0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]\n",
      "  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      "   0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1.]]]\n"
     ]
    }
   ],
   "source": [
    "# 抽取数据\n",
    "x_train, y_train = data_extract(train_data, time_step)\n",
    "x_test, y_test = data_extract(test_data, time_step)\n",
    "print(x_train[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "julian-messenger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_4 (LSTM)                (None, 50)                17200     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 35)                1785      \n",
      "=================================================================\n",
      "Total params: 18,985\n",
      "Trainable params: 18,985\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 建立LSTM模型\n",
    "lstm_model = Sequential()\n",
    "# LSTM层\n",
    "lstm_model.add(LSTM(units=50, input_shape=(time_step, features), activation=\"relu\"))\n",
    "# 输出层\n",
    "lstm_model.add(Dense(units=features, activation=\"softmax\"))\n",
    "lstm_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "powered-clerk",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 核心参数配置\n",
    "lstm_model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "distributed-rogers",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "334/334 [==============================] - 2s 2ms/step - loss: 14096804838310.7500 - accuracy: 0.0325\n",
      "Epoch 2/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 690061395337540096.0000 - accuracy: 0.0180\n",
      "Epoch 3/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1148       \n",
      "Epoch 4/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1312\n",
      "Epoch 5/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1379\n",
      "Epoch 6/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1400\n",
      "Epoch 7/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1402\n",
      "Epoch 8/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1544\n",
      "Epoch 9/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1444\n",
      "Epoch 10/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1584\n",
      "Epoch 11/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1528\n",
      "Epoch 12/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1527\n",
      "Epoch 13/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1270\n",
      "Epoch 14/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1440\n",
      "Epoch 15/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1457\n",
      "Epoch 16/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1450\n",
      "Epoch 17/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1473\n",
      "Epoch 18/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1411\n",
      "Epoch 19/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1372\n",
      "Epoch 20/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1278\n",
      "Epoch 21/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1456\n",
      "Epoch 22/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1403\n",
      "Epoch 23/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1461\n",
      "Epoch 24/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1290\n",
      "Epoch 25/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1466\n",
      "Epoch 26/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1300\n",
      "Epoch 27/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1528\n",
      "Epoch 28/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1319\n",
      "Epoch 29/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1299\n",
      "Epoch 30/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1297\n",
      "Epoch 31/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1287\n",
      "Epoch 32/2000\n",
      "334/334 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.1289\n",
      "Epoch 33/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1418\n",
      "Epoch 34/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1358\n",
      "Epoch 35/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1633\n",
      "Epoch 36/2000\n",
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      "Epoch 1931/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1384\n",
      "Epoch 1932/2000\n",
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      "Epoch 1985/2000\n",
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      "Epoch 1988/2000\n",
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      "Epoch 1989/2000\n",
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      "Epoch 1990/2000\n",
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      "Epoch 1991/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1297\n",
      "Epoch 1992/2000\n",
      "334/334 [==============================] - 1s 3ms/step - loss: nan - accuracy: 0.1248\n",
      "Epoch 1993/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1420\n",
      "Epoch 1994/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1234\n",
      "Epoch 1995/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1335\n",
      "Epoch 1996/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1522\n",
      "Epoch 1997/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1328\n",
      "Epoch 1998/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1276\n",
      "Epoch 1999/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1330\n",
      "Epoch 2000/2000\n",
      "334/334 [==============================] - 1s 4ms/step - loss: nan - accuracy: 0.1513\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fddab8714d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型训练\n",
    "lstm_model.fit(x_train, y_train, batch_size=5, epochs=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "polish-sharing",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict = lstm_model.predict_classes(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "serial-roman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 25, 25, ..., 25, 25, 25])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "excessive-advance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for i in y_train_predict:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "absent-revision",
   "metadata": {},
   "source": [
    "从结果来看，很明显这个不靠谱"
   ]
  }
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